Six open-source machine learning tools you should know
As more organizations incorporate AI and machine learning into their processes, data scientists and analysts must continue to learn about how these techniques solve business problems. Previously on this blog we discussed how particular algorithms may be more appropriate for particular research questions and business problems. Another factor to keep in mind are the many open-source machine learning tools that data scientists use for their work.
Most data scientists are at least familiar with how R and Python programming languages are used for machine learning, but the possibilities do not end there. Open-source machine learning and AI tools include software libraries, toolkits, and suites that aid in executing tasks. Like machine learning algorithms, there is not necessarily a “best” AI or open-source machine learning tool. What you use will (and should) depend on the task you are trying to perform.
Machine learning tools
While there are a growing number of ML tools available, we’ve chosen a few open-source machine learning options that are popular with many data scientists. Below we’ve detailed several machine learning platforms and tools that any new or experienced data scientist should consider exploring.
Scikit-learn is a fundamental tool for anyone performing open-source machine learning tasks using the Python programming language. It is a machine learning library built to be used in conjunction with NumPy and SciPy, Python’s libraries for numerical and scientific computing, respectively.
Supported algorithms in Scikit-learn include classification, regression, clustering, and dimensionality reduction. The library has extensive documentation and an active user base, and is a good open-source machine learning tool for someone new to Python programming and machine learning.
TensorFlow is an end-to-end open-source machine learning tool developed by the Google Brain team meant for large-scale machine learning and numerical computation projects. The platform constructs deep neural networks to conduct tasks like natural language processing (NLP), image recognition, and translation.
TensorFlow is known for being easy to use but also more powerful than many other open-source machine learning libraries or toolkits. This is partially because it uses Python to provide a front-end API for developing applications, while actually running those applications in C++.
One of TensorFlow’s greatest benefits is its ability to deal with the detailed task of implementing rules for each function within an application. TensorFlow will automatically figure out the right order of functions, so a data scientist can stay focused on conceptual questions and the overall purpose of the algorithm. TensorFlow is often used for more complex projects and workflows.
Many data scientists like PyTorch as a tool because of its flexibility and speed. The tool helps users develop dynamic neural networks and can build and change graphs and visualizations as a user continues to work. PyTorch also allows for distributed training (users performing parallel computations), which reduces the time to complete actions.
PyTorch is generally the go-to tool for projects that require quick development and need to be usable in a short period of time.
Ludwig is one of many open-source machine learning toolkits developed by Uber’s AI lab and made available to the open-source community in the past year. It is a toolbox built on TensorFlow and designed for deep learning AI projects. It differs from some of the other Uber tools in that it does not require knowledge of a programming language to use. Instead, users test and train models within a drag-and-drop interface, making the machine learning technology more accessible to all members of an analytics or data science team.
One of Ludwig’s most notable features include its easy-to-understand visualizations, meant to provide the reasoning behind the results of a deep learning algorithm and avoid the “black box” problem. In addition, Ludwig is not meant to be a stand-alone tool and can be integrated with an organization’s other applications via Python API.
Developed by the NLP Research Group at Stanford University, CoreNLP provides a set of tools specifically focused on analyzing human languages. It performs common NLP tasks like sentiment analysis and information extraction. In addition, it can also help data scientists perform more detailed tasks like understanding dependencies in a portion of text (ie. how pronouns relate to each other in a passage), which may result in a clearer understanding of the text.
In addition to English, CoreNLP has NLP models for Arabic, Chinese, French, German, and Spanish, setting it apart from many other commercial NLP tools.
CoreNLP is written in Java and requires Java to run, but can interface with multiple programming languages, including Python. According to some users, one drawback to CoreNLP is that it is optimized for use on local machines rather than in the cloud, and may be better suited for those working on individual projects.
Weka was developed at the University of Waikato in New Zealand and is a popular tool among students and individuals who are just getting started with open-source machine learning. The creators of the tool have curated a series of videos and have written a book on machine learning and data mining techniques.
Weka has a simple GUI allowing users to understand the basics of data mining tasks like data preprocessing, clustering, classification, regression, and visualization without having to focus too much on programming languages. Weka also has a deep learning package, allowing users to attempt more complex analyses while still using the simple interface. Users who prefer to write in a programming language can do that was well. Weka is a Java application but can be used with R or Python via API. Because of its origin in academia, Weka is most commonly used as a teaching tool or for smaller projects.
MLlib is the open-source machine learning library of Apache Spark, the open-source distributed computing framework, and designed for large scale computing environments. As a result, MLlib works best for larger enterprise learning environments.
MLlib contains multiple algorithms that fall under the following categories: regression, classification, dimension reduction, clustering, and recommendations. MLlib has significant language compatibility, allowing data scientists to write applications in Java, Scala, and Python.
The tool also benefits from being a part of the Spark framework, meaning that it allows for quick, iterative computing, and easy deployment that does not require any additional installation. Spark’s large and active community of contributors will also lead to more growth and adoption of MLlib and more resources to support it.
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At Algorithmia, we take an integration-first approach to MLOps. We know that many organizations use a variety of open-source and proprietary tools to support machine learning experimentation and development. Regardless of which tools you’ve used to develop your machine learning models, you can use Algorithmia to deploy them to production and start generating value for your business.
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